Estimating Fisher discriminant error in a linear integrator model of neural population activity

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dc.contributor.authorCalderini, Matias
dc.contributor.authorThivierge, Jean-Philippe
dc.date.accessioned2021-02-23T04:55:32Z
dc.date.available2021-02-23T04:55:32Z
dc.date.issued2021-02-19
dc.identifier.citationThe Journal of Mathematical Neuroscience. 2021 Feb 19;11(1):6
dc.identifier.urihttps://doi.org/10.1186/s13408-021-00104-4
dc.identifier.urihttp://hdl.handle.net/10393/41815
dc.description.abstractAbstract Decoding approaches provide a useful means of estimating the information contained in neuronal circuits. In this work, we analyze the expected classification error of a decoder based on Fisher linear discriminant analysis. We provide expressions that relate decoding error to the specific parameters of a population model that performs linear integration of sensory input. Results show conditions that lead to beneficial and detrimental effects of noise correlation on decoding. Further, the proposed framework sheds light on the contribution of neuronal noise, highlighting cases where, counter-intuitively, increased noise may lead to improved decoding performance. Finally, we examined the impact of dynamical parameters, including neuronal leak and integration time constant, on decoding. Overall, this work presents a fruitful approach to the study of decoding using a comprehensive theoretical framework that merges dynamical parameters with estimates of readout error.
dc.titleEstimating Fisher discriminant error in a linear integrator model of neural population activity
dc.typeJournal Article
dc.date.updated2021-02-23T04:55:32Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
CollectionLibre accès - Publications // Open Access - Publications

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